Enhancing NLP Accuracy: How Hugo Helped an HR Software Company Perfect Job Matching

The Client

A leading human resources software company specializing in talent acquisition and recruitment technology sought to enhance their platform's core functionality. Their software uses natural language processing (NLP) to match job candidates with appropriate openings by analyzing resumes and job descriptions. With a growing client base of Fortune 500 companies and smaller businesses alike, the accuracy of these matches directly impacted both employer satisfaction and job seeker success rates.

The Challenge

The client’s HR software struggled to normalize diverse candidate inputs and job descriptions. Job seekers used varied terminology for the same skills (e.g., “customer management” vs. “client relations”), while hiring managers had inconsistent phrasing.

The AI model often misinterpreted or failed to recognize equivalent qualifications. Subjective skill distinctions (e.g., “team coordination” vs. “project management”) required human judgment, further complicating matching. These issues led to inconsistent talent recommendations, frustrating both employers and job seekers.

The Solution

(1) Specialized Domain Experts for Accurate Classification
Hugo assembled a team of HR and corporate domain experts to ensure precise skill identification across industries and career levels. These specialists were trained in resume analysis and job requirement interpretation, allowing them to capture nuances that generic data labelers might miss.

(2) Three-Annotator Consensus for Reliability
Each resume and job description was independently analyzed by three annotators, who classified skills and mapped them to the client’s taxonomy. Only unanimous classifications were accepted, while disagreements were escalated to quality auditors, reducing bias and improving accuracy.

(3) A Living Knowledge Base for Skill Variability
To manage diverse skill descriptions, Hugo built and continuously refined a “living knowledge base”, documenting equivalent terms, context-dependent meanings (e.g., "Python" as a programming language vs. an animal), and industry-specific terminology. This evolving reference ensured accurate normalization of new and ambiguous skills.

(4) Targeted Improvements for High-Variance Cases
When annotators frequently disagreed on specific skills, Hugo flagged these high-variance cases for deeper review. Targeted training sessions and specialized guidelines were developed to ensure consistent decision-making across all annotators.

(5) Robust Quality Assurance for Ongoing Accuracy
The QA team (Quality Assurance) audited complex cases, weekly calibration sessions aligned interpretations, and direct collaboration with the client’s subject matter experts refined standards as industry terminology evolved.

Through this structured approach, Hugo transformed subjective skill normalization into a scalable, systematic process, significantly enhancing the client's NLP accuracy.

The Results

Higher Algorithm Accuracy – Employed HITL (Human-in-the-Loop) and standardized training data to enhance model precision and reduce false negatives.

• Reduced Missed Matches (-42%) – Targeted variance handling and refined classification guidelines minimized false negatives, ensuring more qualified candidates were recognized.

• Improved Candidate Matching – Used selective fine-tuning and multi-step QA to flag high-variance responses, ensuring accurate skill normalization.

• Expanded Skill Recognition (+1,000+ terms) – Continuous updates to the knowledge base broadened the system’s ability to recognize diverse ways candidates express qualifications.

• Higher Employer Confidence & Efficiency (+27% confidence, -40% screening time) – More accurate matches reduced manual screening, increasing employer trust in the platform.

Hugo’s human-in-the-loop approach proved essential in tackling the subjectivity of resume interpretation, significantly improving job matching and hiring efficiency.